Duration of exposure to natural estrogens is an important risk factor for the development of breast cancer. A novel biological role for estrogen metabolites in breast carcinogenesis has recently been described. Certain metabolites (16alpha-hydroxy and 4-hydroxy derivatives) have been found to damage DNA directly, or to produce oxygen radicals through redox cycling. Others (2-methoxy derivatives that arise from methylation of 2-hydroxy estrogens) appear to be protective. The oxidation reactions are catalyzed by various cytochrome-P450 (CYP) family of monooxygenases, while methylation is controlled by catechol O-methyl transferases (COMTs). Polymorphisms in CYPs and COMTs will be reflected by inter-individual differences in estradiol dispositions (COMTs). Polymorphisms in CYPs and COMTs will be reflected by inter-individual differences in estradiol disposition. A full profile of estradiol metabolism with metabolite kinetics has never been described: a major limitation has been the analytical capability to quantitative the metabolites at physiological or pharmacological levels. We propose here to develop such an analytical capability using novel ultra-high sensitivity liquid chromatography/mass spectrometry methodology that we have discovered recently. We will establish normal circulating concentrations of estradiol, and its conjugates, its major oxidative and methylated metabolites, and conjugates of all the major estradiol metabolites. The kinetic variability of estradiol disposition after a small sublingual dose of estradiol will be established in pre and post-menopausal women with no known risk factors for breast cancer. Sublingual administration will be used so that the plasma estradiol/estrogen concentrations closely match the ratios that have been reported for endogenous estrogens. A pharmacokinetic model will then be developed that will make it possible to define particular metabolic pathways in terms of individual rate- constants. Inter-individual differences in metabolic profiles will be related to particular CYP and COMT genotypes. As a consequence of these studies, a population pharmacokinetic model will be developed so that pharmacogenetic correlations can be explored in greater detail. We will then test a limited sampling strategy for future application to large populations. By identifying metabolic profiles associated with breast cancer risk and by making phenotyped/genotype correlations, we will be in a position to generate testable hypotheses relevant to the condition of breast cancer risk itself. This will ultimately lead to the identification of groups of women who might benefit from chemoprevention strategies (including anti-estrogens), and groups who may be at particular risk for hormone replacement therapy.